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1.  final def !=(arg0: Any): Boolean

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2.  final def ##(): Int

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3.  final def ==(arg0: Any): Boolean

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4.  final def asInstanceOf[T0]: T0

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5.  def clone(): AnyRef

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6.  final def eq(arg0: AnyRef): Boolean

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7.  def equals(arg0: Any): Boolean

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8.  def finalize(): Unit

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9.  final def getClass(): Class[_]

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10.  def hashCode(): Int

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11.  def integrateLambdas(ruleSuiteMap: RuleSuiteMap, lambdas: Map[Id, Seq[LambdaFunction]], globalLibrary: Option[Id] = None): RuleSuiteMap

    Add any of the Lambdas loaded for the rule suites

    Add any of the Lambdas loaded for the rule suites

    globalLibrary

    - all lambdas with this RuleSuite Id will be added to all RuleSuites

12.  def integrateMetaRuleSets(dataFrame: DataFrame, ruleSuiteMap: RuleSuiteMap, metaRuleSetMap: Map[Id, Seq[MetaRuleSetRow]], stablePosition: (String) ⇒ Int, transform: (DataFrame) ⇒ DataFrame = identity): RuleSuiteMap

    Integrates meta rulesets into the rulesuites.

    Integrates meta rulesets into the rulesuites. Note this only works for a specific dataset, if rulesuites should be filtered for a given dataset then this must take place before calling.

    dataFrame

    the dataframe to identify columns for metarules

    ruleSuiteMap

    the ruleSuites relevant for this dataframe

    metaRuleSetMap

    the meta rulesets

    stablePosition

    this function must maintain the law that each column name within a RuleSet always generates the same position

13.  def integrateOutputExpressions(ruleSuiteMap: RuleSuiteMap, outputs: Map[Id, Seq[OutputExpressionRow]], globalLibrary: Option[Id] = None): (RuleSuiteMap, Map[Id, Set[Rule]])

    Returns an integrated ruleSuiteMap with a set of RuleSuite Id -> Rule mappings where the OutputExpression didn't exist.

    Returns an integrated ruleSuiteMap with a set of RuleSuite Id -> Rule mappings where the OutputExpression didn't exist.

    Users should check if their RuleSuite is in the "error" map.

14.  final def isInstanceOf[T0]: Boolean

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15.  final def ne(arg0: AnyRef): Boolean

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16.  final def notify(): Unit

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17.  final def notifyAll(): Unit

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18.  def readLambdasFromDF(lambdaFunctionDF: DataFrame, lambdaFunctionName: Column, lambdaFunctionExpression: Column, lambdaFunctionId: Column, lambdaFunctionVersion: Column, lambdaFunctionRuleSuiteId: Column, lambdaFunctionRuleSuiteVersion: Column): Map[Id, Seq[LambdaFunction]]

    Loads lambda functions

19.  def readMetaRuleSetsFromDF(metaRuleSetDF: DataFrame, columnSelectionExpression: Column, lambdaFunctionExpression: Column, metaRuleSetId: Column, metaRuleSetVersion: Column, metaRuleSuiteId: Column, metaRuleSuiteVersion: Column): Map[Id, Seq[MetaRuleSetRow]]
20.  def readOutputExpressionsFromDF(outputExpressionDF: DataFrame, outputExpression: Column, outputExpressionId: Column, outputExpressionVersion: Column, outputExpressionRuleSuiteId: Column, outputExpressionRuleSuiteVersion: Column): Map[Id, Seq[OutputExpressionRow]]

    Loads output expressions

21.  def readRulesFromDF(df: DataFrame, ruleSuiteId: Column, ruleSuiteVersion: Column, ruleSetId: Column, ruleSetVersion: Column, ruleId: Column, ruleVersion: Column, ruleExpr: Column, ruleEngineSalience: Column, ruleEngineId: Column, ruleEngineVersion: Column): RuleSuiteMap

    Loads a RuleSuite from a dataframe with integers ruleSuiteId, ruleSuiteVersion, ruleSetId, ruleSetVersion, ruleId, ruleVersion and an expression string ruleExpr

22.  def readRulesFromDF(df: DataFrame, ruleSuiteId: Column, ruleSuiteVersion: Column, ruleSetId: Column, ruleSetVersion: Column, ruleId: Column, ruleVersion: Column, ruleExpr: Column): RuleSuiteMap

    Loads a RuleSuite from a dataframe with integers ruleSuiteId, ruleSuiteVersion, ruleSetId, ruleSetVersion, ruleId, ruleVersion and an expression string ruleExpr

23.  final def synchronized[T0](arg0: ⇒ T0): T0

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24.  def toDS(ruleSuite: RuleSuite): Dataset[RuleRow]

    Must have an active sparksession before calling and only works with ExpressionRule's, all other rules are converted to 1=1

    Must have an active sparksession before calling and only works with ExpressionRule's, all other rules are converted to 1=1

    returns

    a Dataset[RowRaw] with the rules flattened out

25.  def toLambdaDS(ruleSuite: RuleSuite): Dataset[LambdaFunctionRow]

    Must have an active sparksession before calling and only works with ExpressionRule's, all other rules are converted to 1=1

    Must have an active sparksession before calling and only works with ExpressionRule's, all other rules are converted to 1=1

    returns

    a Dataset[RowRaw] with the rules flattened out

26.  def toOutputExpressionDS(ruleSuite: RuleSuite): Dataset[OutputExpressionRow]

    Must have an active sparksession before calling and only works with ExpressionRule's, all other rules are converted to 1=1

    Must have an active sparksession before calling and only works with ExpressionRule's, all other rules are converted to 1=1

    returns

    a Dataset[RowRaw] with the rules flattened out

27.  def toRuleSuiteDF(ruleSuite: RuleSuite): DataFrame

    Utility function to easy dealing with simple DQ rules where the rule engine functionality is ignored.

    Utility function to easy dealing with simple DQ rules where the rule engine functionality is ignored. This can be paired with the default ruleEngine parameter in readRulesFromDF

28.  def toString(): String

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29.  final def wait(): Unit

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30.  final def wait(arg0: Long, arg1: Int): Unit

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31.  final def wait(arg0: Long): Unit

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